The invention relates to a method for creating a non-linear, stationary or dynamic model of a control variable of a machine, in particular of a combustion engine or of a partial system thereof, preferably using neuronal nets, over the total area of all operating points of the machine, and whereby the control variable is dependent on a set of input quantities, for example system parameters, system parameters differentiated by time, time-delayed output quantities with feedback and/or preset values at the respective operating point, and whereby for a group of real operating points from the total area of operating points of the machine using a simplified partial model function for this group one output quantity is determined per model function, and the output quantities of each partial model function are added at any operating point in a weighted fashion to an associated weighting function to obtain a total output quantity for the respective operating point, and whereby for all real operating points the difference respectively between the total output quantity and the real value of the control value is determined at this operating point, and in areas of operating points with an absolute value of the difference lying above a preset value a further model function with a further associated weighting function is used for which the absolute value of the difference stays below the preset value.